Training Update (#145)

* split requirement file

* add packages

* update sample image name

* update check condition

* update scripts as per new OD model

* update metadata

* flake update

* flake update

Author: SSaishruthi

tests/training/test_sample_training_response.py

@@ -39,17 +39,17 @@ def test_metadata():
     assert r.status_code == 200
 
     metadata = r.json()
-    assert metadata['id'] == 'ssd_mobilenet_v1_coco_2017_11_17-tf-mobilenet'
-    assert metadata['name'] == 'ssd_mobilenet_v1_coco_2017_11_17 TensorFlow Model'
-    assert metadata['description'] == 'ssd_mobilenet_v1_coco_2017_11_17 TensorFlow model trained on MobileNet'
+    assert metadata['id'] == 'object-detector-ssd_mobilenet_v1'
+    assert metadata['name'] == 'ssd_mobilenet_v1 TensorFlow Object Detector Model'
+    assert metadata['description'] == 'ssd_mobilenet_v1 TensorFlow object detector model'
     assert metadata['type'] == 'Object Detection'
     assert metadata['source'] == 'https://developer.ibm.com/exchanges/models/all/max-object-detector/'
     assert metadata['license'] == 'ApacheV2'
 
 
 def test_predict():
     model_endpoint = 'http://localhost:5000/model/predict'
-    file_path = 'samples/data_1.jpg'
+    file_path = 'samples/a-pen-i-am.jpg'
 
     with open(file_path, 'rb') as file:
         file_form = {'image': (file_path, file, 'image/jpeg')}
@@ -59,11 +59,9 @@ def test_predict():
     response = r.json()
 
     assert response['status'] == 'ok'
-
-    #  Teddy Bear
-    # assert response['predictions'][0]['label_id'] == '88'
-    assert response['predictions'][0]['label'] == 'toy'
-    # assert response['predictions'][0]['probability'] > 0.95
+    # Training run uses fewer samples and epochs to train. Results have randomness due to this.
+    # Checking for all the new classes used in the sample data.
+    assert response['predictions'][0]['label'] in ('toy', 'pen')
 
 
 if __name__ == '__main__':

training/training_code/train-max-model.sh

@@ -26,7 +26,10 @@ echo "Training data is stored in $DATA_DIR"
 # The WML stores work files in the $RESULT_DIR.
 echo "Training work files and results will be stored in $RESULT_DIR"
 
-echo "Installing prerequisite packages ..."
+# Cython is needed for pycococtools installation. Splitting the installation.
+echo "Installing prerequisite packages - 1..."
+pip install --user --no-deps -r training_prerequirements.txt
+echo "Installing prerequisite packages - 2..."
 pip install --user --no-deps -r training_requirements.txt
 
 # ---------------------------------------------------------------

training/training_code/training_prerequirements.txt

@@ -0,0 +1 @@
+Cython

training/training_code/training_requirements.txt

@@ -1,4 +1,3 @@
-Cython
 contextlib2
 pycocotools==2.0.0
 coremltools==2.0
@@ -7,3 +6,6 @@ tensorflowjs==0.8.0
 tensorflow-hub==0.3.0
 h5py==2.8.0
 numpy==1.17.5
+matplotlib
+cycler
+kiwisolver